For the design of QE features, we first borrow the idea of quality estimation feature vectors (QEFVs) and mis-matching features from [1]}, [2]}, and propose a novel feature named Dual Model Feature.
QEFVs can be seen as the approximated transferred knowledge from word prediction to quality estimation, and mis-matching features measure the difference between the model predictions and the given translated sentence.
Apart from the above features, our proposed dual model feature takes the advantage of model-level dual learning, and focuses on modeling the translation information without source contexts involved.
